```
library(sparklyr)
<- spark_connect(master = "local")
sc
<- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
iris_tbl <- iris_tbl %>%
partitions sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
<- partitions$training
iris_training <- partitions$test
iris_test
<- iris_training %>%
mlp_model ml_multilayer_perceptron_classifier(Species ~ ., layers = c(4, 3, 3))
<- ml_predict(mlp_model, iris_test)
pred
ml_multiclass_classification_evaluator(pred)
#> [1] 0.5227273
```

# Spark ML – Multilayer Perceptron

*NULL*

## ml_multilayer_perceptron_classifier

## Description

Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.

## Usage

```
ml_multilayer_perceptron_classifier(
x, formula = NULL,
layers = NULL,
max_iter = 100,
step_size = 0.03,
tol = 1e-06,
block_size = 128,
solver = "l-bfgs",
seed = NULL,
initial_weights = NULL,
thresholds = NULL,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("multilayer_perceptron_classifier_"),
...
)
ml_multilayer_perceptron(
x, formula = NULL,
layers, max_iter = 100,
step_size = 0.03,
tol = 1e-06,
block_size = 128,
solver = "l-bfgs",
seed = NULL,
initial_weights = NULL,
features_col = "features",
label_col = "label",
thresholds = NULL,
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("multilayer_perceptron_classifier_"),
response = NULL,
features = NULL,
... )
```

## Arguments

Arguments | Description |
---|---|

x | A `spark_connection` , `ml_pipeline` , or a `tbl_spark` . |

formula | Used when `x` is a `tbl_spark` . R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details. |

layers | A numeric vector describing the layers – each element in the vector gives the size of a layer. For example, `c(4, 5, 2)` would imply three layers, with an input (feature) layer of size 4, an intermediate layer of size 5, and an output (class) layer of size 2. |

max_iter | The maximum number of iterations to use. |

step_size | Step size to be used for each iteration of optimization (> 0). |

tol | Param for the convergence tolerance for iterative algorithms. |

block_size | Block size for stacking input data in matrices to speed up the computation. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data. Recommended size is between 10 and 1000. Default: 128 |

solver | The solver algorithm for optimization. Supported options: “gd” (minibatch gradient descent) or “l-bfgs”. Default: “l-bfgs” |

seed | A random seed. Set this value if you need your results to be reproducible across repeated calls. |

initial_weights | The initial weights of the model. |

thresholds | Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value `p/t` is predicted, where `p` is the original probability of that class and `t` is the class’s threshold. |

features_col | Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula` . |

label_col | Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula` . |

prediction_col | Prediction column name. |

probability_col | Column name for predicted class conditional probabilities. |

raw_prediction_col | Raw prediction (a.k.a. confidence) column name. |

uid | A character string used to uniquely identify the ML estimator. |

… | Optional arguments; see Details. |

response | (Deprecated) The name of the response column (as a length-one character vector.) |

features | (Deprecated) The name of features (terms) to use for the model fit. |

## Details

When `x`

is a `tbl_spark`

and `formula`

(alternatively, `response`

and `features`

) is specified, the function returns a `ml_model`

object wrapping a `ml_pipeline_model`

which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument `predicted_label_col`

(defaults to `"predicted_label"`

) can be used to specify the name of the predicted label column. In addition to the fitted `ml_pipeline_model`

, `ml_model`

objects also contain a `ml_pipeline`

object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by `ml_save`

with `type = "pipeline"`

to faciliate model refresh workflows. `ml_multilayer_perceptron()`

is an alias for `ml_multilayer_perceptron_classifier()`

for backwards compatibility.

## Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Predictor`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the predictor appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, a predictor is constructed then immediately fit with the input`tbl_spark`

, returning a prediction model.`tbl_spark`

, with`formula`

: specified When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the predictor. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

.

## Examples

## See Also

See https://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms. Other ml algorithms: `ml_aft_survival_regression()`

, `ml_decision_tree_classifier()`

, `ml_gbt_classifier()`

, `ml_generalized_linear_regression()`

, `ml_isotonic_regression()`

, `ml_linear_regression()`

, `ml_linear_svc()`

, `ml_logistic_regression()`

, `ml_naive_bayes()`

, `ml_one_vs_rest()`

, `ml_random_forest_classifier()`